There are two well-known characteristics about text classification. One is that the dimension of the sample space is very high, while the number of examples available usually is very small. The other is that the example vectors are sparse. Meanwhile, we find existing support vector machines active learning approaches are subject to the influence of outliers. Based on these observations, this paper presents a new hybr/d active learning approach. In this approach, to select the unlabelled example(s) to query, the learner takes into account both sparseness and high-dimension characteristics of examples as well as its uncertainty about the examples' categorization. This way, the active learner needs less labeled examples, but still can get a good generalization performance more quickly than competing methods. Our empirical results indicate that this new approach is effective.
